Prediction of Chaotic Time Series Based on SALR Model with Its Application on Heating Load Prediction-Reference-Cited by-同舟云学术

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Prediction of Chaotic Time Series Based on SALR Model with Its Application on Heating Load Prediction

Published:2021-02-22 Issue:9 Volume:46 Page:8171-8187
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Zhang Minghui,Wang Baozhu,Zhou Yatong,Gu Jihao,Wu Yuheng

Funder

Sub-Project of Intelligent Robot under National Key R&D Program of China

Hebei Province Natural Science Foundation

Humanity and Social Science Foundation of Ministry of Education of China

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

https://link.springer.com/content/pdf/10.1007/s13369-021-05407-y.pdf

Reference50 articles.

1. Lun, S.X., Yao, X.S., Qi, H.Y., et al.: A novel model of leaky integrator echo state network for time-series prediction. Neurocomputing 159(jul.2), 58–66 (2015)

2. Al-Shammari, E.T., Keivani, A., Shamshirband, S., et al.: Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm. Energy 95, 266–273 (2016)

3. Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80 (2004)

4. Strauss, T., Wustlich, W., Labahn, R.: Design strategies for weight matrices of Echo state networks. Neural Comput. 24, 3246 (2012)

5. Najibi, E., Rostami, H.: SCESN, SPESN, SWESN: three recurrent neural echo state networks with clustered reservoirs for prediction of nonlinear and chaotic time series. Appl. Intell. 43, 460–472 (2015)

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